Distributed Preemption Decisions: Probabilistic Graphical Model, Algorithm and Near-Optimality

TL;DR

This paper introduces a probabilistic graphical model-based distributed algorithm for connection preemption in networks, achieving near-optimal performance with limited local information exchange, balancing accuracy and complexity.

Contribution

It develops a novel distributed preemption algorithm using probabilistic graphical models that approaches optimal performance with reduced information exchange.

Findings

The algorithm achieves near-optimal preemption decisions.

Trade-offs between performance and information exchange are characterized.

Validation confirms the effectiveness of the proposed approach.

Abstract

Cooperative decision making is a vision of future network management and control. Distributed connection preemption is an important example where nodes can make intelligent decisions on allocating resources and controlling traffic flows for multi-class service networks. A challenge is that nodal decisions are spatially dependent as traffic flows trespass multiple nodes in a network. Hence the performance-complexity trade-off becomes important, i.e., how accurate decisions are versus how much information is exchanged among nodes. Connection preemption is known to be NP-complete. Centralized preemption is optimal but computationally intractable. Decentralized preemption is computationally efficient but may result in a poor performance. This work investigates distributed preemption where nodes decide whether and which flows to preempt using only local information exchange with neighbors. We develop, based on the probabilistic graphical models, a near-optimal distributed algorithm. The algorithm is used by each node to make collectively near-optimal preemption decisions. We study trade-offs between near-optimal performance and complexity that corresponds to the amount of information-exchange of the distributed algorithm. The algorithm is validated by both analysis and simulation.